Your users ask.
Your product does it.
Point it at your documentation and it answers from day one. Connect your API when you're ready and it completes tasks too. And it shows you where your product is falling short. No AI team. No new backend. No rebuild.
Early beta. Rough in places, and we’d rather you saw it anyway.
Your users are stuck.
You can’t see where.
Every release widens the gap between what your product can do and what your users can get it to do.
Features ship, adoption doesn't.
The capability exists. The path to it doesn't. Users never discover the thing you spent a quarter building.
Support answers the same questions forever.
Most tickets aren't bugs. They're navigation, and documentation that went stale two releases ago.
Docs can’t answer “why is mine broken?”
Most questions are about the user’s own data, not your documentation. A help article can’t see their account, so the ticket gets opened anyway.
You can't see what users wanted.
Analytics tell you what people clicked. Nothing tells you what they were trying to do. Or what they couldn't.
Not three different tools.
One that does all three.
It answers like a support agent, reads your live data like an analyst, and completes the task like an assistant, so users get what they came for and support hears about it less.
Bolt on a chatbot, hire a team,
or turn it on today.
A docs chatbot answers questions but can't do anything. Building it yourself works, but it costs you a quarter and two engineers who should be shipping your product.
| Capability | Docs chatbot | Build in-house | CommandPalOur approach |
|---|---|---|---|
| Answers questions from your docs | Yes | Yes | Yes, with sources cited |
| Publishes the docs your users read | No | You write every page | Yes, on your own domain |
| Answers from live product data | No | If you build it | Yes, per user and role |
| Completes tasks in your product | No | If you build it | Yes, with a plan |
| Runs in the user's own permissions | Not applicable | You build it | Always, by design |
| Shows where your product falls short | No | No | Ranked intent gaps |
| Time to live | Days | Months | Minutes |
| AI engineers required | None | Two to three | None |
How it works.
Intent to Action.
No navigation. No manual. Just intent. A user who knows what they want no longer has to know where it lives, so the feature you shipped last quarter stops depending on anyone finding it.
Add your knowledge
A docs URL, PDFs, Markdown, plain text, a ZIP, or bulk-upload a whole folder. Indexing usually takes a few minutes.
Connect your API
Upload your OpenAPI spec and every endpoint is listed for review. Nothing is callable until you approve it: one endpoint, or a whole service at once.
Paste one script tag
A sandboxed iframe that runs in your user's own session. Its theme arrives pre-filled from your site, and every control is still yours.
ThenFrom day one you can see what users are asking for: intent clusters, completion rates, and the gaps where your product had no answer.
Your users' side · every request
User states what they want
No menu, no manual. One sentence in plain language. CommandPal reads the intent and takes over.
A task plan is generated instantly
CommandPal breaks the intent into ordered steps. Everything it can resolve from live context is pre-filled automatically.
Only asks for what it doesn't know
Dynamic UI components surface inline to collect the essential user inputs. Click below to interact.
Every step runs. Nothing is lost.
All 5 steps execute in sequence. A full summary is written: what ran, what changed, what's next.
The best interface for a weekly task
isn’t a chat box.
Chat is right when the request is new. For the things your users do every week, a named button is faster and more predictable. It also advertises what the product can do, so users find capabilities they never knew to ask for.
One tap, not a prompt
Pin what your users do most often and give it a name. It becomes a button in the widget. Nothing to type, nothing to guess, no blank box waiting for the right words.
The sequence is fixed
A pinned action replays the same steps every time instead of re-planning on each run. The steps don’t change; only the user’s inputs do. Reviewed once, then predictable.
Not a recording
Recorded automation breaks the moment an endpoint moves. When your API changes, CommandPal rebuilds the action and sends it back for approval. Your users never hit a broken one. Nothing goes live unapproved, including our own updates.
Everything you'd have to build.
Already built.
Retrieval, tool calling, planning, safe execution, observability. The plumbing teams budget two engineers and half a year to build. And it still isn’t their product.
One ground truth
Web pages, PDFs, Markdown, plain text, ZIPs, or a whole uploaded folder. Deduplicated into a single source and version-controlled, so you can roll back to any earlier version.
Docs you didn’t have to write
A hosted docs site generated from your ground truth, on your own domain via CNAME. Theme controls arrive pre-filled from your own site. Every knob is still yours. Edit any page in the builder.
Your APIs become actions
Upload your OpenAPI spec and your endpoints are listed as candidate tools with AI-written descriptions. Nothing is callable until you approve it.
Live product context
Answers grounded in your real-time state, resolved against the requesting user and their role, not a guess from documentation.
Plan, then execute
An intent becomes a step-by-step plan against your own API. Missing inputs are collected with generated UI cards, and nothing runs until the plan is complete.
Intent gaps, ranked
Every request is classified and clustered into a ranked list of what users are trying to do, including the things your product can’t do yet.
It runs as the user.
Not as a service account.
CommandPal acts inside the session your customer is already signed into, so it inherits their permissions exactly. It cannot see or do anything they couldn’t do themselves. No backend key, no elevated account, nothing for us to over-permission. Your data never reaches the LLM or our servers: the model decides which APIs to call, and the data itself stays inside your platform.
Data stays in your platform
The LLM never sees your records. It receives intent and decides which APIs to call. Your data is read and written inside your own systems.
Plan and execute, separately
The model plans the steps. Execution happens inside your product, in the signed-in user's own session, so the assistant can never do more than the person using it. No OAuth server, no new auth layer: it uses the session your users already have.
Prompt injection, mitigated
Data never flows through the model as instructions, and the model can only call endpoints you have explicitly approved. Safety is structural, not bolted on.
Rather talk it
through first?
Tell us what you’re building and we’ll show you what it looks like with CommandPal in it. On your product, not a demo account.